Beyond Stochastic Gradient Descent for Matrix Completion Based Indoor Localization
In this paper, we propose a high accuracy fingerprint-based localization scheme for the Internet of Things (IoT). The proposed scheme employs mathematical concepts based on sparse representation and matrix completion theories. Specifically, the proposed indoor localization scheme is formulated as a...
Main Authors: | Wafa Njima, Rafik Zayani, Iness Ahriz, Michel Terre, Ridha Bouallegue |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2019-06-01
|
Series: | Applied Sciences |
Subjects: | |
Online Access: | https://www.mdpi.com/2076-3417/9/12/2414 |
Similar Items
-
Provable convergence of Nesterov's accelerated gradient method for over-parameterized neural networks
by: Liu, X., et al.
Published: (2022) -
Generalized Nesterov Accelerated Conjugate Gradient Algorithm for a Compressively Sampled MR Imaging Reconstruction
by: Xiuhan Li, et al.
Published: (2020-01-01) -
Projected Wirtinger gradient descent for spectral compressed sensing
by: Liu, Suhui
Published: (2017) -
AG-SGD: Angle-Based Stochastic Gradient Descent
by: Chongya Song, et al.
Published: (2021-01-01) -
Aperture Shape Generation Based on Gradient Descent With Momentum
by: Liyuan Zhang, et al.
Published: (2019-01-01)